Abstract
We propose a new approach to fault detection and diagnosis in third-generation (3G) cellular networks using competitive neural algorithms. For density estimation purposes, a given neural model is trained with data vectors representing normal behavior of a CDMA2000 cellular system. After training, a normality profile is built from the sample distribution of the quantization errors of the training vectors. Then, we find empirical confidence intervals for testing hypotheses of normal/abnormal functioning of the cellular network. The trained network is also used to generate inference rules that identify the causes of the faults. We compare the performance of four neural algorithms and the results suggest that the proposed approaches outperform current methods.
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References
Prasad, R., Mohr, W., Konäuser, W.: Third Generation Mobile Communication Systems - Universal Personal Communications. Artech House Publishers (2000)
Principe, J.C., Euliano, N.R., Lefebvre, W.C.: Neural and Adaptive Systems: Fundamentals through Simulations. John Wiley & Sons, Chichester (2000)
Ahalt, S., Krishnamurthy, A., Cheen, P., Melton, D.: Competitive learning algorthms for vector quantization. Neural Networks 3, 277–290 (1990)
Kohonen, T.: The self-organizing map. Proceedings of the IEEE 78, 1464–1480 (1990)
Martinetz, T.M., Schulten, K.J.: A ‘neural-gas’ network learns topologies. In: Kohonen, T., Makisara, K., Simula, O., Kangas, J. (eds.) Artificial Neural Networks, pp. 397–402. North-Holland, Amsterdam (1991)
Hammer, B., Rechtien, A., Strickert, M., Villmann, T.: Rule extraction from selforganizing networks. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 877–882. Springer, Heidelberg (2002)
Laiho, J., Kylväjä, M., Höglund, A.: Utilisation of advanced analysis methods in UMTS networks. In: Proceedings of the IEEE Vehicular Technology Conference (VTS/spring), Birmingham, Alabama, pp. 726–730 (2002)
Binzer, T., Landstorfer, F.M.: Radio network planning with neural networks. In: Proceedings of the IEEE Vehicular Technology Conference (VTS/fall), Boston, MA, pp. 811–817 (2000)
Raivio, K., Simula, O., Laiho, J.: Neural analysis of mobile radio access network. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), San Jose, California, pp. 457–464 (2001)
Raivio, K., Simula, O., Laiho, J., Lehtimäki, P.: Analysis of mobile radio access network using the Self-Organizing Map. In: Proceedings of the IPIP/IEEE International Symposium on Integrated Network Management, Colorado Springs, Colorado, pp. 439–451 (2003)
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Barreto, G.A. et al. (2004). Competitive Neural Networks for Fault Detection and Diagnosis in 3G Cellular Systems. In: de Souza, J.N., Dini, P., Lorenz, P. (eds) Telecommunications and Networking - ICT 2004. ICT 2004. Lecture Notes in Computer Science, vol 3124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27824-5_29
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DOI: https://doi.org/10.1007/978-3-540-27824-5_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22571-3
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